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Dustan M. Wheatley, Kent H. Knopfmeier, Thomas A. Jones, and Gerald J. Creager

Abstract

This first part of a two-part study on storm-scale radar and satellite data assimilation provides an overview of a multicase study conducted as part of the NOAA Warn-on-Forecast (WoF) project. The NSSL Experimental WoF System for ensembles (NEWS-e) is used to produce storm-scale analyses and forecasts of six diverse severe weather events from spring 2013 and 2014. In this study, only Doppler reflectivity and radial velocity observations (and, when available, surface mesonet data) are assimilated into a 36-member, storm-scale ensemble using an ensemble Kalman filter (EnKF) approach. A series of 1-h ensemble forecasts are then initialized from storm-scale analyses during the 1-h period preceding the onset of storm reports. Of particular interest is the ability of these 0–1-h ensemble forecasts to reproduce the low-level rotational characteristics of supercell thunderstorms, as well as other convective hazards. For the tornado-producing thunderstorms considered in this study, ensemble probabilistic forecasts of low-level rotation generally indicated a rotating thunderstorm approximately 30 min before the time of first observed tornado. Displacement errors (often to the north of tornado-affected areas) associated with vorticity swaths were greatest in those forecasts launched 30–60 min before the time of first tornado. Similar forecasts were produced for a tornadic mesovortex along the leading edge of a bow echo and, again, highlighted a well-defined vorticity swath as much as 30 min prior to the first tornado.

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C. Paton-Walsh, R. L. Mittermeier, W. Bell, H. Fast, N. B. Jones, and A. Meier

Abstract

The authors report the results of an intercomparison of vertical column amounts of hydrogen chloride (HCl), hydrogen fluoride (HF), nitrous oxide (N2O), nitric acid (HNO3), methane (CH4), ozone (O3), carbon dioxide (CO2), and nitrogen (N2) derived from the spectra recorded by two ground-based Fourier transform infrared (FTIR) spectrometers operated side-by-side using the sun as a source. The procedure used to record spectra and derive vertical column amounts follows the format of previous instrument intercomparisons organized by the Network for the Detection of Atmospheric Composition Change (NDACC), formerly known as the Network for Detection of Stratospheric Change (NDSC).

For most gases the differences were typically around 3%, and in about half of the results the error bars given by the standard deviation of the measurements from each instrument did not overlap. The worst level of agreement was for HF where differences of over 5% were typical. The level of agreement achieved during this intercomparison is a little worse than that achieved in previous intercomparisons between ground-based FTIR spectrometers.

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Sijie Pan, Jidong Gao, David J. Stensrud, Xuguang Wang, and Thomas A. Jones

Abstract

In this study, the ensemble of three-dimensional variational data assimilation (En3DVar) method for convective-scale weather is adopted and evaluated using an idealized supercell storm simulated by the Weather Research and Forecasting (WRF) Model. Synthetic radar radial velocity, reflectivity, satellite-derived cloud water path (CWP), and total precipitable water (TPW) data are produced from the simulated supercell storm and then these data are assimilated into another WRF Model run that starts with no convection. Two types of experiments are performed. The first assimilates radar and satellite CWP data using a perfect storm environment. The second assimilates additional TPW data using a storm environment with dry bias. The first set of experiments indicates that incorporating CWP and radar data into the assimilation leads to a much faster initiation of supercell storms than found using radar data alone. Assimilating CWP data primarily improves the analyses of nonprecipitating hydrometeor variables. The results from the second set of experiments demonstrate the critical importance of the storm environment. When using the biased storm environment, assimilation of CWP and radar data enhances the analyses, but the forecast skill rapidly decreases over the subsequent 1-h forecast. Further experiments show that assimilating the TPW data has a large impact on storm environment that is essential to the accuracy of the storm forecasts. In general, the combination of radar data and satellite data within the En3DVar results in better analyses and forecasts than when only radar data are used, especially for an imperfect storm environment.

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Guillermo A. Baigorria, James W. Hansen, Neil Ward, James W. Jones, and James J. O’Brien

Abstract

The potential to predict cotton yields up to one month before planting in the southeastern United States is assessed in this research. To do this, regional atmospheric variables that are related to historic summer rainfall and cotton yields were identified. The use of simulations of those variables from a global circulation model (GCM) for estimating cotton yields was evaluated. The authors analyzed detrended cotton yields (1970–2004) from 48 counties in Alabama and Georgia, monthly rainfall from 53 weather stations, monthly reanalysis data of 850- and 200-hPa winds and surface temperatures over the southeast U.S. region, and monthly predictions of the same variables from the ECHAM 4.5 GCM. Using the reanalysis climate data, it was found that meridional wind fields and surface temperatures around the Southeast were significantly correlated with county cotton yields (explaining up to 52% of the interannual variability of observed yields), and with rainfall over most of the region, especially during April and July. The tendency for cotton yields to be lower during years with atmospheric circulation patterns that favor higher humidity and rainfall is consistent with increased incidence of disease in cotton during flowering and harvest periods under wet conditions. Cross-validated yield estimations based on ECHAM retrospective simulations of wind and temperature fields forced by observed SSTs showed significant predictability skill (up to 55% and 60% hit skill scores based on terciles and averages, respectively). It is concluded that there is potential to predict cotton yields in the Southeast by using variables that are forecast by numerical climate models.

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C. A. Balfour, M. J. Howarth, D. S. Jones, and T. Doyle

Abstract

An evolving coastal observatory has been hosted by the National Oceanography Centre at Liverpool, United Kingdom, for more than nine years. Within this observatory an instrumented ferry system has been developed and operated to provide near-surface scientific measurements of the Irish Sea. Passenger vessels such as ferries have the potential to be used as cost-effective platforms for gathering high-resolution regular measurements of the properties of near-surface water along their routes. They are able to operate on an almost year-round basis, and they usually have a high tolerance to adverse weather conditions. Examples of the application of instrumented ferry systems include environmental monitoring, the generation of long-term measurement time series, the provision of information for predictive model validation, and data for model assimilation purposes.

This paper discusses the development of an engineering system installed on board an Irish Sea passenger ferry. Particular attention is paid to explaining the engineering development required to achieve a robust, automated measuring system that is suitable for long-term continuous operation. The ferry, operating daily between Birkenhead and Belfast or Dublin, United Kingdom, was instrumented between December 2003 and January 2011 when the route was closed. Measurements were recorded at a nominal interval of 100 m and real-time data were transmitted every 15 min. The quality of the data was assessed. The spatial and temporal variability of the temperature and salinity fields are investigated as the ferry crosses a variety of shelf sea and coastal water column types.

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S. A. Good, G. K. Corlett, J. J. Remedios, E. J. Noyes, and D. T. Llewellyn-Jones

Abstract

The trend in sea surface temperature has been determined from 20 yr of Advanced Very High Resolution Radiometer Pathfinder data (version 5). The data span the period from January 1985 to December 2004, inclusive. The linear trends were calculated to be 0.18° ± 0.04° and 0.17° ± 0.05°C decade−1 from daytime and nighttime data, respectively. However, the measured trends were found to be somewhat smaller if version 4.1 of the Pathfinder data was used, or if the time series of data ended earlier. The influence of El Niño on global temperatures can be seen clearly in the data. However, it was not found to affect the trend measurements significantly. Evidence of cool temperatures after the eruption of Mount Pinatubo in 1991 was also observed.

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T. Vukicevic, T. Greenwald, M. Zupanski, D. Zupanski, T. Vonder Haar, and A. S. Jones

Abstract

This study focuses on cloudy atmosphere state estimation from high-resolution visible and infrared satellite remote sensing measurements and a mesoscale model with explicit cloud prediction. The cloud state is defined as 3D spatially distributed hydrometeors characterized with microphysical properties: mixing ratio, number concentration, and size distribution. The Geostationary Operational Environmental Satellite-9 (GOES-9) imager visible and infrared measurements were used in a new four-dimensional variational data assimilation (4DVAR) mesoscale algorithm for a warm continental stratus cloud system case to test the impact of these observations on the cloud simulation. The new data assimilation algorithm includes the Regional Atmospheric Modeling System (RAMS) with explicit cloud state prediction, the associated adjoint system, and an observational operator for forward and adjoint integrations of the GOES radiances. The results show positive impact of GOES imager measurements on the 3D cloud short-term simulation during and after the assimilation. The impact was achieved through sensitivity of the radiances to the cloud droplet mixing ratio at observation time and a 4D correlation between the cloud and atmospheric thermal and dynamical environment in the forecast model. The dynamical response to the radiance observations was through enhanced large mesoscale vertical mixing while horizontal advection was weak in the case of stable continental stratus evolution.

Although the current experiments show measurable positive impact of the cloudy radiance measurements on the stratus cloud simulation, they clearly suggest the need to further address the problem of negative cloud cover forecast errors. These errors were only weakly corrected in the current study because of the small sensitivity of the visible and infrared window radiances to the cloud-free atmosphere.

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Thomas A. Jones, Patrick Skinner, Kent Knopfmeier, Edward Mansell, Patrick Minnis, Rabindra Palikonda, and William Smith Jr.

Abstract

Forecasts of high-impact weather conditions using convection-allowing numerical weather prediction models have been found to be highly sensitive to the selection of cloud microphysics scheme used within the system. The Warn-on-Forecast (WoF) project has developed a rapid-cycling, convection-allowing, data assimilation and forecasting system known as the NSSL Experimental WoF System for ensembles (NEWS-e), which is designed to utilize advanced cloud microphysics schemes. NEWS-e currently (2017–18) uses the double-moment NSSL variable density scheme (NVD), which has been shown to generate realistic representations of convective precipitation within the system. However, very little verification on nonprecipitating cloud features has been performed with this system. During the 2017 Hazardous Weather Testbed (HWT) experiment, an overestimation of the areal coverage of convectively generated cirrus clouds was observed. Changing the cloud microphysics scheme to Thompson generated more accurate cloud fields. This research undertook the task of improving the cloud analysis generated by NVD while maintaining its skill for other variables such as reflectivity. Adjustments to cloud condensation nuclei (CCN), fall speed, and collection efficiencies were made and tested over a set of six severe weather cases occurring during May 2017. This research uses an object-based verification approach in which objects of cold infrared brightness temperatures, high cloud-top pressures, and cloud water path are generated from model output and compared against GOES-13 observations. Results show that the modified NVD scheme generated much more skillful forecasts of cloud objects than the original formulation without having a negative impact on the skill of simulated composite reflectivity forecasts.

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Thomas A. Jones, David Stensrud, Louis Wicker, Patrick Minnis, and Rabindra Palikonda

Abstract

Assimilating high-resolution radar reflectivity and radial velocity into convection-permitting numerical weather prediction models has proven to be an important tool for improving forecast skill of convection. The use of satellite data for the application is much less well understood, only recently receiving significant attention. Since both radar and satellite data provide independent information, combing these two sources of data in a robust manner potentially represents the future of high-resolution data assimilation. This research combines Geostationary Operational Environmental Satellite 13 (GOES-13) cloud water path (CWP) retrievals with Weather Surveillance Radar-1988 Doppler (WSR-88D) reflectivity and radial velocity to examine the impacts of assimilating each for a severe weather event occurring in Oklahoma on 24 May 2011. Data are assimilated into a 3-km model using an ensemble adjustment Kalman filter approach with 36 members over a 2-h assimilation window between 1800 and 2000 UTC. Forecasts are then generated for 90 min at 5-min intervals starting at 1930 and 2000 UTC. Results show that both satellite and radar data are able to initiate convection, but that assimilating both spins up a storm much faster. Assimilating CWP also performs well at suppressing spurious precipitation and cloud cover in the model as well as capturing the anvil characteristics of developed storms. Radar data are most effective at resolving the 3D characteristics of the core convection. Assimilating both satellite and radar data generally resulted in the best model analysis and most skillful forecast for this event.

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Thomas A. Jones, Xuguang Wang, Patrick Skinner, Aaron Johnson, and Yongming Wang

Abstract

A prototype convection-allowing system using the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model and employing an ensemble Kalman filter (EnKF) data assimilation technique has been developed and used during the spring 2016 and 2017 Hazardous Weather Testbeds. This system assimilates WSR-88D reflectivity and radial velocity, geostationary satellite cloud water path (CWP) retrievals, and available surface observations over a regional domain with a 3-km horizontal resolution at 15-min intervals, with 3-km initial conditions provided by an experimental High-Resolution Rapid Refresh ensemble (HRRR-e). However, no information on upper-level thermodynamic conditions in cloud-free regions is currently assimilated, as few timely observations exist. One potential solution is to also assimilate clear-sky satellite radiances, which provide information on mid- and upper-tropospheric temperature and moisture conditions. This research assimilates GOES-13 imager water vapor band (6.5 μm) radiances using the GSI-EnKF system to take advantage of the Community Radiative Transfer Model (CRTM) integration. Results using four cases from May 2016 showed that assimilating radiances generally had a neutral-to-positive impact on the model analysis, reducing humidity bias and/or errors at the appropriate model levels where verification observations were present. The effects on high-impact weather forecasts, as verified against forecast reflectivity and updraft helicity, were mixed. Three cases (9, 22, and 24 May) showed some improvement in skill, while the other (25 May) performed worse, despite the improved environment. This research represents the first step in designing a high-resolution ensemble data assimilation system to use GOES-16 Advanced Baseline Imager data, which provides additional water vapor bands and increased spatial and temporal resolution.

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